class_docstr: A class for writing training data to a file.
docstr: Generates text for the entity attributes.
  
  Args:
      text: The text that is annotated with the entity
      entity: Entity data
      short_allowed: If `True`, allow shorthand annotation with parenthesis
  
  Returns:
      The annotation text that should follow the given text
docstr_tok: ["Generates","text","for","the","entity","attributes",".","Args",":","text",":","The","text","that","is","annotated","with","the","entity","entity",":","Entity","data","short_allowed",":","If","`","True","`",",","allow","shorthand","annotation","with","parenthesis","Returns",":","The","annotation","text","that","should","follow","the","given","text"]
code: def generate_entity_attributes(
      text: Text, entity: Dict[Text, Any], short_allowed: bool = True
  ) -> Text:
      """Generates text for the entity attributes.
  
      Args:
          text: The text that is annotated with the entity
          entity: Entity data
          short_allowed: If `True`, allow shorthand annotation with parenthesis
  
      Returns:
          The annotation text that should follow the given text
      """
      entity_text = text
      entity_type = entity.get(ENTITY_ATTRIBUTE_TYPE)
      entity_value = entity.get(ENTITY_ATTRIBUTE_VALUE)
      entity_role = entity.get(ENTITY_ATTRIBUTE_ROLE)
      entity_group = entity.get(ENTITY_ATTRIBUTE_GROUP)
  
      if entity_value and entity_value == entity_text:
          entity_value = None
  
      use_short_syntax = (
          short_allowed
          and entity_value is None
          and entity_role is None
          and entity_group is None
      )
  
      if use_short_syntax:
          return f"({entity_type})"
      else:
          entity_dict = OrderedDict(
              [
                  (ENTITY_ATTRIBUTE_TYPE, entity_type),
                  (ENTITY_ATTRIBUTE_ROLE, entity_role),
                  (ENTITY_ATTRIBUTE_GROUP, entity_group),
                  (ENTITY_ATTRIBUTE_VALUE, entity_value),
              ]
          )
          entity_dict = OrderedDict(
              [(k, v) for k, v in entity_dict.items() if v is not None]
          )
  
          return f"{json.dumps(entity_dict)}"
  
imports: import abc
  import json
  from collections import OrderedDict
  from pathlib import Path
  import rasa.shared.nlu.training_data.util
  from rasa.shared.constants import INTENT_MESSAGE_PREFIX
  from rasa.shared.nlu.constants import INTENT, ENTITY_ATTRIBUTE_VALUE, ENTITY_ATTRIBUTE_START, ENTITY_ATTRIBUTE_END, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_GROUP, ENTITY_ATTRIBUTE_ROLE
  import rasa.shared.utils.io
  import typing
  from typing import Text, Dict, Any, Union, List
  from collections import defaultdict
label_desc: Use image node_id 6 for calling the TrainingDataWriter obj's underlying member method code with example usage: obj.generate_entity_attributes(text, entity, short_allowed) and returns: str, entity_type, str, json